An empirical study of MetaCost using boosting algorithms

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1810, Issue , 2000, Pages 413-425

  Ting, Kai Ming ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING;

BOOSTING ALGORITHM; COST-SENSITIVE ALGORITHM; EMPIRICAL STUDIES; EXPECTED COSTS; INTERNAL COSTS;

LEARNING ALGORITHMS;

EID: 84960442798     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45164-1_42     Document Type: Conference Paper

References (9)

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2. Blake, C., Keogh, E. & Merz, C.J. (1998), UCI Repository of machine learning databases [http://www.ics.uci.edu/˜mlearn/MLRepository.html]. Irvine, CA: University of California, Dept. of Information and Computer Science.
3. Domingos, P. (1999), MetaCost: AGen eral Method for Making Classifiers Cost-Sensitive, in Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining, pp. 155-164, ACM Press.
4. Michie, D., D.J. Spiegelhalter, & C.C. Taylor (1994), Machine Learning, Neural and Statistical Classification, Ellis Horwood Limited.
5. Quinlan, J.R. (1993), C4.5: Program for Machine Learning, Morgan Kaufmann.
6. Quinlan, J.R. (1996), Bagging, boosting, and C4.5, in Proceedings of the 13th National Conference on Artificial Intelligence, pp. 725-730, AAAI Press.
7. Schapire, R.E., Y. Freund, P. Bartlett, & W.S. Lee (1997), Boosting the margin: Ane w explanation for the effectiveness of voting methods, in Proceedings of the Fourteenth International Conference on Machine Learning, pp. 322-330.
8. Ting, K.M. (2000), An Empirical Study of MetaCost using Boosting Algorithms. Technical Report C01/00, Deakin University.
9. Ting, K.M. & Z. Zheng (1998), Boosting Cost-Sensitive Trees, Proceedings of the First International Conference on Discovery Science, LNAI-1532, Berlin: Springer-Verlag, pp. 244-255.